This paper investigates the metric time series classification problem. Distance functions between time series are constructed using the dynamic time warping method. This method aligns two time series and builds a dissimilarity set. The vector-function of distance between the time series is a set of statistics. It describes the distribution of the dissimilarity set. The object feature description in the classification problem is the set of selected statistics values of the dissimilarity set. It is built between the object and all the reference objects. The additional information about the dissimilarity distribution improves the classification quality. We propose a classification method and demonstrate its result on the classification problem of the human physical activity time series from the mobile phone accelerometer.
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Data from accelerometer. Available at: http://sourceforge.net/p/mlalgorithms/TSLearning/data/preprocessed large.csv (accessed November 15, 2016).
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Goncharov, A.V., Strijov, V.V. Analysis of Dissimilarity Set Between Time Series. Comput Math Model 29, 359–366 (2018). https://doi.org/10.1007/s10598-018-9415-4
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DOI: https://doi.org/10.1007/s10598-018-9415-4